The Library of Integrative Network-based Cellular Signatures (LINCS) is an NIH Common Fund program. The idea is to perturb different types of human cells with many different types of perturbations such as: drugs and other small molecules; genetic manipulations such as knockdown or overexpression of single genes; manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more. These perturbations are applied to various types of human cells including induced pluripotent stem cells from patients, differentiated into various lineages such as neurons or cardiomyocytes. Then, to better understand the molecular networks that are affected by these perturbations, changes in level of many different variables are measured including: mRNAs, proteins, and metabolites, as well as cellular phenotypic changes such as changes in cell morphology. The BD2K-LINCS Data Coordination and Integration Center (DCIC) is commissioned to organize, analyze, visualize and integrate this data with other publicly available relevant resources. In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies. We then present data processing and normalization methods to clean and harmonize LINCS data. This follow discussions about how data is served as RESTful APIs. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.

Taught By

Avi Ma’ayan, PhD

Director, Mount Sinai Center for Bioinformatics

Transcript

[MUSIC] In this lecture, I will give a brief introduction to the LINCS Data Signature Generation Centers. The LINCS program is now at phase two. In phase one there were two centers, and now in phase two we have six. There are two centers at the Broad Institute of MIT, a center at Harvard Medical School, a center at Oregon Health Sciences. A center at Mount Sinai in New York and a center at UCI, some centers have components in multiple sites. These centers collect high-content data from human cells treated with many perturbations profiled across many cell types. And perturbations can be drugs, genetic manipulations, micro-environment or disease. Each center has a name, and here you can see for example for the UCI center, the name is NeuroLINCS. And it has five components of different institutions, including UCI, UCLA, MIT, MGH, and Gladstone. For the LINCS Consortium, there is one Data Coordination and Integration Center, that is us. Next, I will describe in some detail what the data generation centers are doing. So let's begin with NeuroLINCS. NeuroLINCS is studying neurodegenerative diseases using IPS cells. The focus is on two related diseases ALS and SMA. These are two similar devastating diseases from a family of modern neuron disorders. ALS also called Lou Gehrig disease, about 30,000 Americans have this disease with about 5,000 new cases occurring in the United States each year. To study ALS and SMA the NeuroLINCS Center is first generating induced pluripotent stem cells from individuals with the disease and compare these IPSCs created from people without the disease. IPSCs are somatic cells extracted from the patient typically through a biopsy and then converted to stem cells. Once we have patient specific stem cells, we can differentiate these to motor neurons. And this is giving us the ability to study the disease in a dish. I will have more information about stem cell reprogramming into IPSCs in a short video later. So this is an overview of what NeuroLINCS is doing. The IPSCs are generated at Cedar Sinai in UCLA, and then sent to the other site for profiling. They're using epigenomics at MIT, proteomics at UCLA, transcriptomics with RNA-seq at UCI, and imaging at Gladstone. They make cells from four normal individuals, four ALS patients, and four SMA patients. Once all this data is collected they hope to put it all together to have a better view of the mechanisms of these diseases. And this can potentially lead to the development of drugs for these disorders which are desperately needed. The next center that I will talk about is the MEP-LINCS, which is Micro Environment Perturbagen LINCS Center. The primary method used by MEP-LINCS is a new technology called MEMA, Micro Environment Micro Arrays. The technology allows the formation of wells where the surface of each well is coded with an extracellular ligand such as integrin or fibronectin. The cells are growing in these different surfaces, respond differently to perturbations such as those that can be induced by drugs. That is, the study had a micro environment influences cellular responses. The cells are imaged with robotics, and different methods are used to assess changes in the morphology. MEP-LINCS is planning to run these experiments in many conditions, and test these in 20 cancer cell lines. MEMA is a first stage screen, once the MEMA results from the initial screen are done, the most interesting conditions, those that generated unique responses. Will be picked for further analysis using high-content imaging, proteomics, and transcriptomics. The proteomics will be done at MD Anderson with reverse-phase protein arrays. The transcriptomics will be done at the Broad Institute using the L1000 technology. The data produced by MEP-LINCS is deposited into the Sage Bionetwork platform. The team at MEP-LINCS developed a shiny r-data explorer that can provide a initial overview of the data they are collecting. Next, I will discuss the Drug Toxicity Signature Center at Mount Sinai. This LINCS data signature generation center is based on a publication in science Translational Medicine from 2013. In this paper, Shan Zhao, et al, analyzed the FDA adverse event reporting system to identity drug combinations that potentially mitigate major side effects. Their goal is to understand the molecular cellular mechanisms of those major side effects. And how combinations of drug affect the cell signaling networks that cause the mitigation of the side effect by that other drug. So they will take IPSCs from healthy patients and differentiate them into myocytes, liver, or neurons. Cells will be then treated with a single or combination of drugs that are known to cause side effects or mitigate side effects. The drug combinations are based on the combinations that they found in the initial study. mRNA and protein expression will be measured before and after the treatment with the drugs. You can read more about the DToxS Center in this URL. The next center that I will discuss is the Harvard Medical School LINCS Center. The goal of this center is to develop network level signatures of cellular responses for many therapeutic drugs. But also investigational drugs such as kinases inhibitors, chromatin modifiers and other soluble components of the microenvironment. They use multiple assays to profile those networks. These include multiplex proteomics, gene expression, and phenotypic assays for cell viability and morphological response. Many of the measurements are done at the single cell, and are utilizing imaging technologies. They are using a diverse array of cell types, mostly tumor cancer cell lines, but also some primary human cells, fibroblast and IPSCs. You can read more about their center at this website that provides access to the date and make tools. So here are some more details about the assay that they are using. So KINOMEscan is an assay that we will discuss in more detail later. About the binding affinity of small molecules to a panel of many kinases. Cell viability assays are performed to measure cell survival after treatment with small molecules. Phosphorylation state and protein levels are profiled with various technologies. They also collaborate with the Broad Institute Transcriptomic Center using the L1000 technology to measure gene expression after perturbation with the same conditions. They're using high content imaging and live cell imaging under many of those conditions. We can also measure cytokines in the extracellular environment before and after perturbation to assess the changes in cell secretion of those cytokines. Next, I will briefly describe the LINCS Proteomics Characterization Center for Signaling and Epigenetics. Like the name suggests this center is profiling the signaling and epigenetics regulatory layer that we discussed earlier. They're using two mass spectrometry based assays called P100 and GCP. GCP stands for Global Chromatin Profiling. P100 is a targeted proteomic assay that measures the level of 96 key phosphosites, selected to represent a diverse set of cell signaling pathways. GCP is a similar targeted proteomics assay that profile the level of post-translation modifications on histones. The conditions, cell types, small molecules, concentrations and time points are for the most part matching a subset of the conditions that are applied by the Broad Transcriptomics Center which we'll discuss next. These efforts will produce a global view of three regulatory layers under many of the same defined conditions, cell types, and perturbation space. Putting these data together to obtain a global picture is highly desirable, because it can produce better predictive mechanistic models of how human cells internally work. So to summarize the LINCS PCCSE center they are profiling many cells types with the same small molecules. Using the two targeted with the same small molecules that are profiled with the Broad Transcriptomics Center using the two targeted proteomic assays P100 and GCP. The goal is to better understand cell signaling and epigenetics regulation. They selected the P100 targeted phospho-sites based on known pathways and literature. The idea is that they will be able to measure enough variables that we should be able to define a unique cellular state to figure out how cells transition from one state to the other. The final LINCS data generation center, the LINCS Trancriptomics Center at the Broad is essentially the new Connectivity Map project. It is the same group that published the original Connectivity Map paper in 2006. The center which was part of phase one also, is doing high throughput transcriptomic profiling of human cells with many drugs, and many genetic perturbations using the L1000 technology. Since we have several lectures dedicated to this resource and this technology. We will not go into details here, but you can explore more about this center in this URL. So each of those LINCS DSGC's centers have their own web portal. Each of these web portals can be accessed through our www.lincsproject.org website. [MUSIC]

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